How to Use AI Notetakers to Improve Marketing Knowledge Bases

Discover how AI Notetakers can help marketing teams scale-up conversations with customers, internal experts, and supply chain partners to create a continuous stream of human insights to enrich marketing knowledge bases and AI marketing activities.

TL;DR

AI Notetakers like Otter and Granola can transform content marketing by capturing human conversations and turning them into structured insights that power more consistent, creative, and scalable marketing programs.

Best practices for AI Notetakers and marketing knowledge base enrichment:

• Talk to customers, partners, and front-line staff, not just executives

• Plan questions like a journalist to uncover richer stories

• Add tags in interviews and/or transcripts to improve AI performance

• Combine transcripts in structured docs for better AI analysis

• Clearly define transcripts vs. transcript summaries

• Protect data and share responsibly to build trust

• Build custom AI bots aligned to key content formats such as LinkedIn posts, articles

• Experiment with AI interview agents to automate certain conversations

Together, these steps help create a solid foundation for an AI-Forward 'Content Engine' that turns human dialogue into scalable, insight-rich storytelling.

The AI-Enabled Content Engine

The term Content Engine describes a modern, collaborative approach to content marketing where human insights and experiences combine with AI systems to create a continuous cycle of creation, expansion, and reuse. The goal is to increase scale, consistency, and accuracy while giving teams more capacity to focus on format innovation and compelling storytelling.

Built around five phases — Insights, Planning, Production, Expansion, and Tracking — Content Engines support the full content lifecycle, from anchor posts that extend across multiple formats and channels to recycling and, ultimately, archival. The bring together tools like AI Notetakers, Research Assistants, custom bots, and agents to enable deeper storytelling that connects with every kind of audience, including skimmers, swimmers, and deep divers. Use cases for Content Engines include demand gen campaigns, YouTube channels, social media marketing, and thought leadership programs.

Best Practices: AI Notetakers for Marketing Knowledge Bases

What follows is a summary of the hard-fought lessons learned by Chris McLellan, Principal at Friends Electric, an AI-forward marketing consultancy:

1. Cast Your Net Wide

Don't just interview the leadership team! Talk to everyone in the ecosystem, from warehouse managers to long-term customers and channel partners.

Front line employees have just as many insights to offer as the C Suite, if not more. Partners can also add interesting perspectives on your product or service.

AI Notetakers for marketing content shine when they capture authentic language from people outside your usual marketing bubble. The most unexpected roles often surface the most relatable insights.

Let the conversations surprise you.

2. Plan Questions Carefully

You are now basically an in-house journalist. Share a short interview document (no more than ten questions) in advance and ask participants what topics they would like to discuss.

After seven years producing the Ask AI Podcast, I can say this step always improves comfort levels and leads to much better conversations.

3. Stack Your Transcripts

AI Research Assistants often limit how many files you can upload, so combine multiple call transcripts into a single Google Doc. For example, I recently produced a 600+ page Google Doc for a client project that contained roughly fifteen 45 minute stakeholder conversations. It's best to combine related transcripts by theme, such as customer stories, leadership interviews, or product team updates for quick retrieval later and improved context for AI tools.

If you use this approach, it's also important to give the master transcript doc a clear title like "Transcripts: Customer Calls 2025" plus a table of contents that's produced by setting H1 headers for each conversation. You should also add a "What this document is for" section at the start. This is all to help AI tools utilize the docs effectively as a source.

Once you have your transcripts in order, the next step is to decide how you will summarize and label them.

4. Define Summaries vs. Transcripts

Transcripts are your raw data and the only reliable source for verbatim quotes. Summaries are interpretations, useful for spotting patterns but never for attribution.

It's critical that you and the custom content bots you create understand the following point: Quotes used in content must come only from transcripts, never from summaries.

In order to keep them straight, apply a clear and consistent syntax to your document titles so your team and any AI tools knows which files are summaries, and which they can treat as source truth.

For example:

• TRANSCRIPT_CustomerCall_March2025

• SUMMARY_CustomerCall_March2025

A consistent naming convention prevents mix-ups and keeps your dataset clean, producing more reliable, trustworthy content.

5. Choose the Right Notetaker for the Job

Each AI Notetaker tool has its strengths. I recently switched from Granola to Google Meet because it was already part of my toolset, and I could replicate Granola’s summaries with a Custom GPT. But you do you, as they say ;)

Here's a mini breakdown of some popular tools:

• Google Meet: built-in AI summaries and notes after each call

• Fathom: free, accurate Zoom recordings

• Granola: clean interface for meetings, summary templates, tool agnostic

• Otter: live captioning and speaker identification

Choose based on recording access, transcription quality, summarization features, and export options. The right tool depends on your content creation process, not brand loyalty.

6. Tag The Insights

Add short tags or notes to your recordings and transcripts to make key themes easy to find later. You can do this in three ways:

• If your AI Notetaker lets you highlight or mark moments during a meeting, add tags like “customer pain point” or “feature idea” as you go.

  
  

• If you are reviewing the transcript afterward, add small tags in brackets e.g. [customer_painpoint] after important lines, or include a short tag list at the end of chunks of transcript.

  
  

• It is also perfectly ok to pause during a recorded conversation and literally say out loud something like “note to self” followed by a quick reminder or thought. These small asides give your transcript more context and help you remember why something mattered in the moment. This becomes especially helpful after long stretches of dialogue, where adding a one-line summary ensures key takeaways aren’t lost

Using audio and text tags make your transcripts easier to search, organize, and analyze, and they give your AI tools a clearer sense of what's most important.

7. Secure & Share Responsibly

Always record conversations with consent, store transcripts securely, and redact sensitive data before uploading to any AI system. Good data ethics equals good marketing.

By integrating AI Notetakers into your workflow, you can turn everyday conversations into a continuous stream of marketing insight and inspiration.

8. Experiment with AI Interview Agents

While person-to-person conversations are always best, it is worth experimenting with AI Interview Agents like Synthflow that can run simple conversations automatically.

These tools can collect input from subject matter experts or partners who may find it difficult to join a live recording session but want to contribute. AI Interview Agents do not replace real dialogue, but they can help capture insights that might otherwise never make it into your content pipeline, especially during early research or idea discovery.

9. Activate Your Transcripts

Capturing conversations is only step one. The real value comes from activating those transcripts by using AI tools that turn insight into content.

Create Custom Bots, powered by custom instructions, that can draft content directly from transcripts or summaries stored in their knowledge base. Examples include:

• A Case Study Bot that pulls insights and quotes from transcripts

• A LinkedIn Post Bot that summarizes a conversation anonymously

• A Webinar Bot that drafts agendas based on hot button issues that arose

Build an AI Research Assistant dedicated to content-related decision-making and final content analysis, essentially an AI Editor in Chief. Its role is to ensure every piece of draft content aligns with your brand voice, tone, product playbook, and strategic direction before publication.

Together, these tools complete the loop from conversation capture to activation.

They form the foundation of what I call AI-Forward Content Marketing, which uses structured insights and trusted data sources to fuel a continuous, scalable content engine.

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Let’s Connect

I'm Chris, an experienced growth marketer and early adopter of AI marketing technologies. I collaborate with founders, startups, scale-ups, and established businesses to build AI-first marketing functions that help teams punch above their weight and spend more time improving the customer experience.

If you're ready to explore how to design or scale a marketing function like this, get in touch for a free discovery call.